TITLE:
Research on Dynamic Forecast of Flowering Period Based on Multivariable LSTM and Ensemble Learning Classification Task
AUTHORS:
Chao Chen, Xingwei Zhang, Shan Tian
KEYWORDS:
Multivariable LSTM, Ensemble Learning Combination Strategy, Random Forest, Adaboost
JOURNAL NAME:
Agricultural Sciences,
Vol.11 No.9,
September
24,
2020
ABSTRACT: The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. Flowering forecast is not only an important part of the construction of agro-meteorological index system, but also an important part of the meteorological service system. In this paper, by analyzing local meteorological data and phenological data of “Red Fuji” apples in Fen County, Linfen City, Shanxi Province, with the help of machine learning and neural networks, we proposed a method based on the combination of time series forecasting and classification forecasting is proposed to complete the dynamic forecasting model of local flowering in Ji County. Then, we evaluated the effectiveness of the model based on the number of error days and the number of days in advance. The implementation shows that the proposed multivariable LSTM network has a good effect on the prediction of meteorological factors. The model loss is less than 0.2. In the two-category task of flowering judgment, the idea of combining strategies in ensemble learning improves the effect of flowering judgment, and its AUC value increases from 0.81 and 0.80 of single model RF and AdaBoost to 0.82. The proposed model has high applicability and accuracy for flowering forecast. At the same time, the model solves the problem of rounding decimals in the prediction of flowering dates by the regression method.